





"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
       import torch

       from transformers.generation import DisjunctiveConstraint

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[Any]:
            # For consistency across different places the DisjunctiveConstraint is called,
            # dc.token_ids is a list of integers. It is also initialized only by integers.

            lowerCAmelCase    		=			[[1, 2, 4], [1, 2, 3, 4]]
            lowerCAmelCase    		=			DisjunctiveConstraint(lowercase					)
            self.assertTrue(isinstance(dc.token_ids ,		lowercase					)					)

            with self.assertRaises(lowercase					):
                  DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]					)					)

            with self.assertRaises(lowercase					):
                  DisjunctiveConstraint([torch.LongTensor([1, 2, 4]					), torch.LongTensor([1, 2, 3, 4, 5]					)]					)





      def        _snake_case     (			self					)						->	Dict:
            # We can't have constraints that are complete subsets of another. This leads to a preverse
            # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
            # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
            # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
            # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
            lowerCAmelCase    		=			[[1, 2], [1, 2, 3, 4]]

            with self.assertRaises(lowercase					):
                  DisjunctiveConstraint(lowercase					)  # fails here





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			[[1, 2, 3], [1, 2, 4]]

            lowerCAmelCase    		=			DisjunctiveConstraint(lowercase					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(1					)
            lowerCAmelCase    		=			stepped is True and completed is False and reset is False
            self.assertTrue(lowercase					)
            self.assertTrue(not dc.completed					)
            self.assertTrue(dc.current_seq == [1]					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(2					)
            lowerCAmelCase    		=			stepped is True and completed is False and reset is False
            self.assertTrue(lowercase					)
            self.assertTrue(not dc.completed					)
            self.assertTrue(dc.current_seq == [1, 2]					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(3					)
            lowerCAmelCase    		=			stepped is True and completed is True and reset is False
            self.assertTrue(lowercase					)
            self.assertTrue(dc.completed					)  # Completed!
            self.assertTrue(dc.current_seq == [1, 2, 3]					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

            lowerCAmelCase    		=			DisjunctiveConstraint(lowercase					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(1					)
            self.assertTrue(not dc.completed					)
            self.assertTrue(dc.current_seq == [1]					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(2					)
            self.assertTrue(not dc.completed					)
            self.assertTrue(dc.current_seq == [1, 2]					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(4					)
            self.assertTrue(not dc.completed					)
            self.assertTrue(dc.current_seq == [1, 2, 4]					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(5					)
            self.assertTrue(dc.completed					)  # Completed!
            self.assertTrue(dc.current_seq == [1, 2, 4, 5]					)

            dc.reset()

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(1					)
            self.assertTrue(not dc.completed					)
            self.assertTrue(dc.remaining() == 3					)
            self.assertTrue(dc.current_seq == [1]					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(2					)
            self.assertTrue(not dc.completed					)
            self.assertTrue(dc.remaining() == 2					)
            self.assertTrue(dc.current_seq == [1, 2]					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			dc.update(5					)
            self.assertTrue(dc.completed					)  # Completed!
            self.assertTrue(dc.remaining() == 0					)
            self.assertTrue(dc.current_seq == [1, 2, 5]					)













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






